Project Summary This is an application for an Administrative Supplement award to an existing project, using fMRI-guided TMS to increase central executive function in older adults. This award will provide our team with the support necessary to extend our existing fMRI-TMS paradigm to patients with a prodromal form of Alzheimer?s Disease (AD) known as amnestic Mild Cognitive Impairment (MCI), and investigate the role of brain health factors in mediating the TMS-related memory performance benefits associated with communication between a network of frontoparietal brain regions in these populations. To achieve these goals, we have developed a plan to expand our existing project and augmented our scientific team to add Dr. Richard O?Brien, Chair of the Neurology Department at Duke, and Dr. Jeffrey Browndyke, who both have extensive experience conducting multimethodological investigations of aging and neurodegenerative disease, including AD and MCI, the population examined in the current proposal. The focus on focal neurostimulation at only a single site represents a fundamental gap in the approach of memory-based neurostimulation therapies. Neurostimulation affects multiple sites within a cortical network, but these global effects have not been used as targets for stimulation because of limited knowledge about what influence these localized sites have on global changes in brain state. To address this problem, we will use multimodal neuroimaging tools and network modeling approaches developed though the parent U01 project, to demonstrate how focal neurostimulation improves the efficacy of TMS for enhancing memory function. These goals will be addressed in the Administrative Supplement under our two specific aims. First, we will use network-guided TMS to optimize memory success based in the frontoparietal network (FPN) in a new group of MCI patients. We will implement a new form of TMS targeting that involves modeling of the global network to understand how the controllability of a stimulation site evokes changes in widespread brain networks. Second, we will identify structural and functional factors affecting the efficacy of individualized network-guided TMS to ameliorate deficits in MCI. By creating a multimodal model of neural deficits related to MCI, we will adjust network-guided TMS to demonstrate how the MCI brain might compensate for these neural deficits. The parent U01 project has made foundational advances towards these goals, as we have demonstrated the ability of to selectively enhance and reduce working memory performance in healthy older adults. In the current Administrative Supplement we will extend this paradigm to a group of MCI participants in order to test the hypothesis that excitatory rTMS to the working memory network can provide positive outcomes for patients with pre-clinical AD. The proposed work will provide an important tool for studying the stability and controllability of network connectivity of memory states in the aging brain, as well as new information on the effectiveness of brain stimulation technologies as a therapeutic approach for cognitive decline.